Systems and methods for facilitating contact center coaching

ABSTRACT

Systems and methods for facilitating contact center coaching are provided. In this regard, a representative method includes: receiving information corresponding to a condition indicating that the agent is failing to meet pre-established criteria; scheduling a coaching meeting with the agent in order to address the condition; and scheduling training for the agent based, at least in part, on information obtained during the coaching meeting, the training being selected to address the condition; wherein the information corresponding to the condition, the information corresponding to the coaching meeting, and information corresponding to the training is integrated into a coaching session form

CROSS REFERENCE TO RELATED APPLICATION

This application is a Continuation-In-Part application, which claims the benefit of a priority to U.S. patent application entitled Systems and Methods for Workforce Optimization and Integration, Ser. No. 11/359,356, filed on Feb. 22, 2006, which is incorporated by reference herein.

TECHNICAL FIELD

The present disclosure relates to workforce optimization of contact centers.

DESCRIPTION OF THE RELATED ART

The business of a contact center is to provide rapid and efficient interaction between agents and customers (or prospective customers). Existing solutions require the purchase of multiple hardware and software components, typically from different vendors, to achieve the business goals of the contact center. The use of separate systems of components leads to a variety of problems. For instance, each system typically has its own method of configuration and its own user interface. Thus, exchanging data between the systems requires additional work by someone at the contact center.

Furthermore, contact centers are continually tasked with striking a balance between service quality, efficiency, effectiveness, revenue generation, cost cutting, and profitability. As a result, today's contact center agents are charged with mastering multiple data sources and systems, delivering consistent service across customer touch points, up-selling, cross-selling, and saving at-risk customers, while winning new ones.

SUMMARY

Systems and methods for facilitating contact center coaching are provided. In this regard, an embodiment of a system for coaching an agent of a contact center comprises a coaching system that is configured to communicate with at least one application of a workforce optimization platform via a communication network. The coaching system is operative to: receive information corresponding to a condition indicating that the agent is failing to meet pre-established criteria; schedule a coaching meeting with the agent in order to address the condition; schedule training for the agent based, at least in part, on information obtained during the coaching meeting, the training being selected to address the condition; and integrate the information corresponding to the condition, the information corresponding to the coaching meeting, and information corresponding to the training into a coaching session form

An embodiment of a method for coaching an agent of a contact center comprises: receiving information corresponding to a condition indicating that the agent is failing to meet pre-established criteria; scheduling a coaching meeting with the agent in order to address the condition; and scheduling training for the agent based, at least in part, on information obtained during the coaching meeting, the training being selected to address the condition; wherein the information corresponding to the condition, the information corresponding to the coaching meeting, and information corresponding to the training is integrated into a coaching session form

Another embodiment of a method for coaching an agent of a contact center comprises: receiving information corresponding to a condition indicating that the agent is failing to meet pre-established criteria; responsive to receiving the information, accessing a coaching session template; automatically filling in at least a portion of the coaching session template with information corresponding to the agent such that a communication session form associated with the agent is generated; scheduling a coaching meeting with the agent in order to address the condition; selecting a training session based, at least in part, on information obtained during the coaching meeting; and automatically scheduling the agent for the training session based, at least in part, on information corresponding to a current schedule of the agent and an estimated time for conducting the training session such that the information corresponding to the condition, the information corresponding to the coaching meeting, and information corresponding to the training session is integrated into the coaching session form.

BRIEF DESCRIPTION OF THE DRAWINGS

Many aspects of the disclosure can be better understood with reference to the following drawings. The components in the drawings are not necessarily to scale, emphasis instead being placed upon clearly illustrating the principles of the present disclosure.

FIG. 1 is a block diagram of a contact center environment.

FIG. 2 is a diagram of one embodiment of the integrated process for optimizing operations at a contact center.

FIG. 3 is a high-level view of components in one embodiment of an integrated contact center system.

FIG. 4 shows a point of integration between the work force manager (WFM) and the quality monitor of FIG. 3.

FIG. 5 shows another point of integration between the work force manager (WFM) and the quality monitor of FIG. 3.

FIG. 6 shows several points of integration between the WFM and the learning component of FIG. 3.

FIG. 7 shows several points of integration between the performance manager and the learning component of FIG. 3.

FIG. 8 shows a point of integration between the WFM and the performance manager of FIG. 3.

FIG. 9 shows another point of integration between the WFM and the performance manager of FIG. 3.

FIG. 10 shows components of the analytics function of FIG. 3.

FIG. 11 is a flowchart depicting functionality associated with an embodiment of a coaching system.

FIG. 12 is a flowchart depicting functionality associated with another embodiment of a coaching system.

FIGS. 13-23 are exemplary graphical user interfaces that can be displayed to a user of an embodiment of a coaching system.

FIG. 24 is a schematic diagram depicting an embodiment of a coaching system implemented by a computer.

DETAILED DESCRIPTION

The systems and methods described herein provide integrated solutions for performing workforce management, quality monitoring, e-learning, performance management, analytics and coaching functionality. Combining at least some of the aforementioned functionality as a unified integrated solution, delivered through a single platform, enables users to gain more insight and make smarter decisions faster about sales, service, and overall operations. This takes contact center tools beyond the traditional “suite” approach to a true single workforce optimization platform.

In this regard, some embodiments represent a convergence of at least some of six contact center technology segments that work together in support of a greater customer service strategy. In short, the integrated workforce optimization platforms disclosed herein can potentially integrate: (1) Quality Monitoring/Call Recording—voice of the customer; the complete customer experience across multimedia touch points; (2) Workforce Management—strategic forecasting and scheduling that drives efficiency and adherence, aids in planning, and helps facilitate optimum staffing and service levels; (3) Performance Management—key performance indicators (KPIs) and scorecards that analyze and help identify synergies, opportunities and improvement areas; (4) e-Learning—training, new information and protocol disseminated to staff, leveraging best practice customer interactions and delivering learning to support development; (5) Analytics—deliver insights from customer interactions to drive business performance; and/or (6) Coaching—identification of substandard performance, scheduling of supervisor intervention and training, agent feedback and report generation. These six segments can become part of an interwoven and interoperable solution, enabling contact centers to transition from reactive cost centers to proactive, information-rich departments that deliver strategic value to the organization.

Further, the integrated workforce optimization platforms disclosed herein provide closed-loop systems for continuous performance improvement, enabling contact centers to: establish realistic forecasts and performance goals; schedule and deploy the right number of staff with the appropriate skills; capture customer interactions in their entirety by recording all calls, or recording based on business rules, or on-demand, or randomly; measure performance to identify execution issues and excellence; analyze customer interactions to investigate opportunities for optimizing use of people, processes and technologies; take action by delivering targeted coaching, training or re-engineering processes; and/or refine forecasts and performance goals based on the collected data.

Reference will now be made to several exemplary embodiments. With respect to coaching, however, many of the aspects relevant to coaching are dedicated to a distinct section below.

One embodiment of the integrated process and system disclosed herein begins with planning and establishing goals—from both an enterprise and center perspective—to ensure alignment and objectives that complement and support one another. Next comes forecasting and scheduling of the workforce to ensure optimum service levels. Then recording and measuring performance are utilized, leveraging quality monitoring/call recording to assess service quality and the customer experience.

Next, the process/system analyzes and identifies opportunities and correlates them the with contact center or organization's KPIs and scorecards. Then, coaching, e-learning and company-specific “best practices” (documented through captured customer interactions) make it possible to address skill and knowledge gaps efficiently and effectively—as well as quickly communicate policy or procedural changes across the center—enabling the contact center to achieve success in whatever terms it chooses to define. Rather than arbitrarily sending e-learning training segments and hoping agents use them, contact centers can use advanced workforce management forecasting and scheduling to select the best time to administer training (which is proven to be more effective than classroom or group learning) as well as free supervisors to work one-on-one with agents.

Quality monitoring scores, including insights from analytics and/or analytical analysis of structured, unstructured, or aggregated data, can next be fed into a workforce management to produce staffing models that prevent companies from unknowingly scheduling one shift with all the top performers, for example. As a result, some embodiments of the workforce management component of the process/system of the present invention can provide a higher level of consistent service across shifts.

As can be seen, while each technology segment delivers value, integration is the key: together the segments deliver greater impact than the sum of their individual parts. Utilizing them separately limits the contact center's potential to become a strategic business asset.

The integrated systems for workforce optimization disclosed herein potentially solve many deficiencies in today's maturing contact center industry. For instance, at an operational level, centers are focused on optimizing customer sales/service representative (CSR) performance. In the process, they may be working under constraints, such as cost control and infrastructures that provides only bare essentials. They may also face the challenge of matching demand with resources, retaining effective agents, prioritizing coaching/training, and delivering consistent customer experiences. Leveraging an integrated system and its components, such as forecasting and scheduling, voice/screen capture/recording, evaluations and best practice training, enables them to focus on reducing risk, decreasing average handle time, improving quality scores, driving down average time to answer, ensuring adherence and managing occupancy.

At a more advanced level, contact centers are focused on optimizing contact center performance. They face the challenge of balancing productivity with quality, increasing center-driven revenue, standardizing service across touch points, and growing transaction complexities. Contact centers are examining such metrics as first call resolution, shrinkage, up-selling and cross-selling, and customer satisfaction as driven though the contact center. As disclosed herein, the forecasting and scheduling, adherence, business rules-driven recording, lesson management, and agent/organizational scorecard functionality—for example—unites contact center experiences, provides flexible scheduling, and promotes the initiation of a performance improvement culture.

The subject matter disclosed herein is related to the subject matter disclosed in several pending U.S. patent applications. One is entitled “Enterprise Manager for Recorders from a Central Point of Administration,” Attorney Docket No. 762301-1180, filed Feb. 22, 2006, and entirely incorporated by reference herein. The subject matter of the 1180 application is centralized administration of voice, video, and data recorders, and enabling role-based access control of recorders which do not have role-based security concepts.

Another is “Systems and Methods for Scheduling Call Center Agents using Quality Data,” Attorney Docket No. 762301-1280, filed Feb. 22, 2006, and entirely incorporated by reference herein.

Another is “Systems and Methods for Scheduling Call Center Agents using Quality Data,” Attorney Docket No. 762301-1280, filed Feb. 22, 2006, and entirely incorporated by reference herein.

Another is “Systems and Methods for Scheduling Call Center Agents using Quality Data and Correlation-Based Discovery,” Attorney Docket No. 762301-1010, filed Feb. 22, 2006, and entirely incorporated by reference herein.

Another is “System and Method for Integrating Learning Systems and Scorecards Systems”, Attorney Docket No. 762301-1090, filed Feb. 22, 2006, and entirely incorporated by reference herein.

Another is “System and Method for Integrating Learning Systems and Workforce Management Systems”, Attorney Docket No. 762301-1150, filed Feb. 22, 2006, and entirely incorporated by reference herein.

Another is U.S. application Ser. No. 10/136,705, entitled “Method and System for Presenting Events Associated with Recorded Data Exchanged between a Server and a User,” and entirely incorporated by reference herein. The subject matter of the '705 application includes capturing and graphically displaying events that occur during an interaction between a customer and an agent. A reviewer is presented with a summarized voice interaction session, in the form of a call timeline, including a list of event identifiers. The reviewer selects one of the event identifiers in the timeline, and the interaction session, starting with the selected event, is presented to the user. The user could choose to start listening to the exchange at an event by selecting the event.

Another is U.S. application Ser. No. 10/137,480, entitled “Method and System for Selectively Dedicating Resources for Recording Data Exchanged between Entities Attached to a Network,” filed on Apr. 30, 2002, and entirely incorporated by reference herein. The subject matter of the '480 application includes determining whether to use an active tap or a passive tap to record data passing through a particular node based upon an objective for recording as noted by predefined business rules.

Another is U.S. Ser. No. 10/136,735, entitled “Methods and Systems for Categorizing and Cataloguing Recorded Interactions,” filed on Apr. 30, 2002, and entirely incorporated by reference herein. The subject matter of the '735 application includes categorizing data upon storing the captured data. The categories are based upon predefined business rules for storing captured data.

Another is U.S. application Ser. No. 10/061,469, entitled “Method, Apparatus, and System for Capturing Data Exchanged between a Server and a User,” filed on Jan. 31, 2002, and entirely incorporated by reference herein. The subject matter of the '469 application includes capture of exchange data by a capture module that operates independently from the server and the user.

Another is U.S. application Ser. No. 10/061,489, entitled “Method, Apparatus, and System for Processing Data Captured during Exchanges between a Server and a User,” filed on Jan. 31, 2002, and entirely incorporated by reference herein. The subject matter of the '489 application includes selective recordation of captured data based upon whether the data satisfies predetermined business rules.

Another is U.S. application Ser. No. 10/061,491, entitled “Method, apparatus, and system for replaying data selected from among data captured during exchanges between a server and a user,” filed on Jan. 31, 2002, and entirely incorporated by reference herein. The subject matter of the '491 application includes replaying data captured during a session, wherein search criteria are based upon business rules.

The following is a list of other U.S. utility applications which may include related subject matter, each of which is incorporated by reference: U.S. utility application, entitled, “Method and Apparatus for Long-Range Planning,” having Ser. No. 09/899,895, filed Oct. 3, 2002; U.S. utility application entitled, “Interface System and Method of Building Rules and Constraints For a Resource Scheduling System,” having Ser. No. 09/680,131, filed Oct. 2, 2000; U.S. Utility Application entitled, “System and Method for Complex Schedule Generation,” having Ser. No. 09/825,589, filed Apr. 3, 2001; U.S. utility application entitled, “Method and Apparatus for Long-Range Planning,” having Ser. No. 09/899,895, filed Jul. 5, 2001; U.S. utility application entitled, “Method and Apparatus for Multi-Contact Scheduling,” having Ser. No. 11/037,604, filed Jan. 18, 2005; and U.S. Utility application entitled, “Method and Apparatus for Concurrent Error Identification in Resource Scheduling,” having Ser. No. 11/237,456, filed Sep. 27, 2005.

Contact Center Environment

FIG. 1 is a block diagram of a contact center environment 100. The contact center 100 is staffed by agents who handle incoming and/or outgoing contacts. Although the traditional and most common form of contact is by phone, other types of contacts are becoming more common (e.g., text chat, web collaboration, email, and fax). An agent workspace includes an agent phone 110 and a workstation computer 120. A network 130 connects one or more of the workstations 120.

A call router 140 distributes incoming contacts to available agents. When the contacts are made by traditional phone lines, the call router 140 operates by connecting outside trunk lines 150 to agent trunk lines 160. In this environment, the call router 140 may be implemented by an automatic call distributor (ACD), which queues calls until a suitable agent is available. Other types of contacts, such as Voice over Internet Protocol (VoIP) calls and computer-based contacts (e.g., chat, email) are routed over one or more data networks. These contacts are distributed over network 130 to one of the agent workstations 120.

During a customer contact, the agent interacts with one or more applications running on the workstation 120. Example workstation applications give the agent access to customer records, product information, ordering status, and transaction history, for example.

The business purpose of a contact center is to provide rapid and efficient interaction between agents and customers. To achieve this purpose, a contact center follows a business process having stages, in that one stage affects subsequent stages.

In a conventional contact center business process, there is a relatively high degree of separation between stages. In contrast, in the integrated contact center business process 200 (FIG. 2) described here, multiple stages are connected into a loop, with each stage of the process feeding input into another stage down the line.

FIG. 2 is a diagram of an embodiment of the integrated process for optimizing operations at a contact center (200), in which several interfaced organizations are combined as a single integrated operational process and/or platform. In the first stage (210), the business goals of the contact center are defined. Goals are defined in terms of metrics that describe how the contact center is expected to perform. Some metrics relate to expected revenue, such as revenue/hour or revenue/agent. Other metrics relate to service level, such as time-to-answer and rate of first-call resolution. Persons familiar with contact center operations will understand these and many other business goals and metrics.

The first stage (210) may also include campaign planning. Profiles for campaigns are defined, for example, by inbound or outbound, how many contacts are expected, date and duration of the campaign, and what sorts of agent skills are needed.

Information about the goals and campaign(s) produced by the first stage (210) is provided to the second stage (220). In the second stage (220), a workforce of agents is scheduled to staff the campaign(s). In determining the number of agents scheduled for a campaign, the goals/metrics and campaign characteristics from the first stage (210) are considered. The schedule also uses as input a workload forecast, which predicts contact volume during each interval of the campaign, based on historical data. Using this schedule, the contact center manager deploys the appropriate number and mix of agents during the campaign times.

The output of the second stage (220) is the customer-agent interactions that occur during a campaign. The third stage (230) measures or assesses the interactions in various ways. One typical assessment (“adherence”) measures how well an agent complied with contact center policies (e.g., call duration). In the third stage (230), at least a portion of the interactions are recorded and then examined. This examination produces a variety of quality metrics that assess an agent's skills in various categories (product knowledge, selling, listening, etc.)

The various assessments are provided as input to the fourth stage (240). In this stage, these inputs are analyzed in various ways. The analysis may rate interactions on a “good” to “bad” scale, considering the customer point of view, the business point-of-view, or both. For example, a contact that resulted in a sale would be an indicator of a “good” interaction while a contact that exceeded average duration would be an indicator of a “bad” interaction.

Once “bad” interactions are identified, an attempt is made to determine a root cause. In some cases, the root cause may lie with an agent (e.g., weak product skills). In other cases, the cause may be in the contact center infrastructure or operations (e.g., customer database is slow). The cause might also be rooted in a business process of the enterprise that is sponsoring the campaign. For example, the billing process used by the enterprise, or the process by which the enterprise dispatches field service units could be the cause.

The fifth stage (250) uses the analysis produced by the fourth stage (230) to adapt and change operations accordingly. Agent skills can be improved by training in the deficient areas. The information may be used to change an aspect of contact center operations, or to make a recommendation to the sponsoring enterprise for it to change its processes or operations. The results of the analysis, as well as the raw metrics used as input to the analysis, are combined into data sets (“scorecards”) that allow the contact center operators to determine whether or not the business goals are met and whether the metrics show progress toward the goals or away from the goal (“trending”). These data sets are provided as input to the first stage (210), which closes the feedback loop of the integrated contact center business process 200.

FIG. 3 is a high-level view of components in an embodiment of an integrated contact center system 300. The integrated system 300 includes two or more of the following components: a work force manager (WFM) 310; a quality monitoring component 320; a learning component 330; and a performance management component 340. These components (310-340) cooperate to implement the integrated contact center business process (200) as described earlier.

As will be described, combining agent quality metrics from the quality monitor 320 (e.g., synchronous such as voice, asynchronous such as e-mail or chat) with WFM 320 (e.g., agent planning, scheduling) may provide insight that contact center supervisors can use to confirm the value provided by agents to the business as a whole.

The WFM 310 performs many functions related to the agent workforce. For example, WFM 310 can: schedule single, multiple, or virtual contact centers across multiple time zones;

accommodate a dedicated, blended, or task-switching environment; schedule meetings or training without impact on service levels; allow agents to bid for shifts and provide input into their schedules; automate compliance with government and union regulations; create centralized forecasts and schedules with a single point of control over the entire network, or decentralized schedules that allow for decision-making at individual sites; schedule based on skill priorities that align with the contact center's routing strategy; and create and schedule teams as a unit to support training and accommodate employee preferences.

The functionality of the entire WFM 310 is typically divided among several applications, executables, processes, or services. A forecast and scheduling component (350) calculates staffing levels and agent schedules based on historical interaction (contact) patterns. A tracking component (355) provides a contact center supervisor or manager with information about agent activities and agent-customer interactions, both historical and real-time. An adherence component (360) supplies the supervisor with information on how well each agent complies with call center policies. For example, once schedules are created, the contact center should ensure that agents follow the schedules.

Most preferably, the adherence component 360 provides a real-time view of every activity across each channel in the contact center, including those in the front and back office, so supervisors/contact centers can see how their staff spends its time. In an enhancement, alerts can be set to notify supervisors when agents are out-of-adherence and exception management can help ensure agents are correctly recognized for work they have performed.

The quality monitor 320 includes a content recorder (370) for recording agent-customer interactions. The content recorder 370 can be configured to capture all interactions, or a selected set of interactions based on user-defined business rules.

The content recorder 370 can capture voice and data interactions from both traditional and IP telephony environments and can handle high-volume recording for compliance and sales verification. The content recorder 370 can also record all voice transactions across multiple sites, or randomly capture a subset of transactions that may be of particular interest, as well as record contacts on-demand. Using the content recorder 370 a user can record all contacts or establish advanced business rules to capture only those transactions of particular interest. User-defined business rules can trigger the recordings, initiate enterprise collaboration by notifying individuals or groups of the captured contacts and emerging trends, and allow users to assign attributes or “tags” to the contacts for quick identification. All data related to a customer interaction—including navigation of automated systems, agent keystrokes and desktop activities—can be stored automatically in folders for search and retrieval. Different users in an enterprise can share and review transactions, as well as hear customer feedback first-hand.

The quality manager 320 stores the interactions in an interactions database 375, which may include descriptive information as well as recorded content. Contact center personnel play back some of the interactions and use an evaluation component (380) to score the agent in various categories (product knowledge, selling, listening, etc.)

Furthermore, contact center supervisors and quality analysts can then tap into these recorded interactions to review, evaluate, and score agent performance. An analytics component (385) can analyze interactions in various ways, including the use of speech analytics. Examples of analysis include categorizing calls based on content, analyzing a call against an expected call pattern and reporting exceptions to the pattern, and providing a visualization layer for recorded interactions that displays other data attributes such as agent activities coincident with call events.

The learning component 330 allows a contact center manager to develop training lessons for agents and assign lessons to agents. The learning component 330 provides automated training processes by identifying, scheduling, and delivering online learning directly to agent desktops.

The lesson content can include recorded interactions, which can be used to create a library of best practices for training agents and other personnel. Using actual interactions, a contact center can develop E-learning content specific to the organization. In an enhancement, these training lessons can include assessments to help track and measure agent performance, skill acquisition, and knowledge retention.

The learning component 330 can also deliver targeted learning sessions over a network, using e-mail, or a hyperlink to a Web site, or directly to the agent desktop. Supervisors can select the appropriate training sessions from a library of courseware or create sessions themselves using a contact-editing feature. Then supervisors can assign course material and monitor completion automatically.

The performance manager 340 displays key performance indicators (KPIs), which can be predefined on a scorecard. The scorecard, which can be role-appropriate, provides a statistical measure of how well an agent or group of agents is performing (against their goals). The KPI metrics are derived from quality evaluations and/or WFM call routing data.

A centralized administration component 390 consolidates agent administration across the various components into a single point of entry, and provides a single logon to all components for agents and administrators. The administration component 390 may also include a centralized reporting component, even across multiple sites. A consistent user interface 395 reduces training time on the various system components.

An integrated contact center system such as system 300 allows contact center analysts to quickly access the right information. Such an integrated system allows valuable and previously undiscovered information to be uncovered. This new level of visibility into contact center operations should allow personnel make better decisions faster.

FIG. 4 shows a point of integration between two components of the integrated contact center system 300, the WFM 310 and the quality monitor 320. Conventional call center systems provide an “interactions” application that allows playback of recorded interactions and live monitoring of interactions. Importantly, these conventional systems did not integrate interactions with WFM adherence information. The integration between the WFM 310 and the quality monitor 320 described in FIG. 4 allows a supervisor to “drill down” and examine a particular recorded interaction from a display of agent activity and/or adherence information.

In this disclosure, the term “interaction” refers to a record of the content of agent activities related to a call. Note that agent activities are not limited to audio of the call itself. Other forms of media are included. Examples of other types of interactions are: video recording of the agent; application activity on the agent's workstation 120; web pages delivered to the agent and/or customer during collaborative sessions; messages delivered through e-mail, instant messaging, or other messaging technologies. Also, the agent activities in an interaction are not limited to the duration of the call, but can occur after the call (a state called “wrap up” or “research”).

The tracking component 355 of the WFM 310 provides information about agent activities to the WFM adherence component 360. Agent activities, which describe work activities performed by agents, are collected from various sources. The call router 140 (FIG. 1) reports agent call states (Available, After-Call-Work, etc.). An application monitor on agent workstation 120 tracks agent activity on the workstation (e.g., switching between applications, screen data, keyboard input, etc.).

The adherence component 360 displays a view (410) of agent activities, typically one agent per line, with activities arranged across a timeline. Exceptions to agent adherence (e.g., non-compliance with contact center policy) are displayed in conjunction with the activities and the timeline.

The adherence component 360 obtains a list (420) of recorded interactions available for agents during the displayed time period. This list of interactions is presented to the user in the same adherence view (410).

From this adherence view, a user can “drill down” to a recorded interaction by selecting (430) the interaction from the list, and then activating a playback tool. The adherence component 360 retrieves (440) the selected interaction from the interactions database 375, and the interaction is then played back using an appropriate application (e.g. media player, desktop activity player, web content player). A user can also select an agent activity that is presently occurring and either record on demand (450) or live monitor (460) the selected activity.

Integration between the WFM 310 and the quality monitor 320 is further described in the U.S. patent application “System and Method for Integrated Display of Recorded Interactions and Call Agent Data,” Attorney Docket Number 762301-1160, filed the same day and by the same assignee as the instant application.

FIG. 5 shows an additional point of integration between the WFM 310 and the quality monitor 320, in which agent activity, adherence, and/or scheduling information is used to trigger selective recording in a selective recording environment, or to perform smart selection of recording for evaluation in a total recording environment. In a conventional quality monitor 320, the content recorder 370 can be configured to record a certain number, or percentage, of agent-customer interactions. This parameter is typically fixed for the duration of a campaign, though it can vary from one campaign to the next.

In the integrated system 500, the WFM 310 generates call recording parameters 510 based on information contained in the forecast 520 (e.g., call volume and call type) and/or the schedule 530. The recording parameters 510 are provided to the content recorder 370 in the quality monitor 320. This integration allows the content recorder 370 to adapt recording behavior during a campaign.

As an example of how this feature is useful to a contact center, consider a marketing campaign that starts on a Monday and lasts all week. It is expected that call quality for agents on this campaign will be relatively low on Monday, since the material is new to the agents. By the end of the week, the agents are more familiar with the material, so that agent quality scores are expected to increase.

The recording parameters 510 provided to the content recorder 370 in the integrated system 500 allow a contact center manager to increase the percentage of interactions recorded at the start of the campaign, and to reduce the percentage as the campaign progresses. recording parameters 510 can be further associated with one agent, or a set of agents, so that inexperienced agents (e.g., agents with low scores) have a higher percentage of recorded interactions as compared to more experienced agents.

Other examples of using WFM data to determine recording behavior include: trigger or select recording based on relative elapsed time from the beginning of the shift; trigger or select recording before or after specific activities (e.g., after lunch or before break activity); and trigger or select recording based on adherence data (e.g., agent is on call but not adhering to schedule).

FIG. 6 shows several points of integration between the WFM 310 and the learning component 330. The learning component 330 includes lessons 610. Each lesson 610 is designed to improve an agent's competence in a particular area. Lessons are assigned, either manually or automatically, through a lesson assignment component 620, which communicates information about the assignment (630) to the scheduler 350 in the WFM 310. In one embodiment, the information 630 includes an agent identifier, a lesson identifier, a lesson duration, and a lesson completion date. After receiving the lesson assignment information 630, the scheduler 350 modifies the schedule 530 to include a training activity for the identified agent. If possible, the new training activity is scheduled before the lesson completion date.

An agent receives training through a lesson presentation function 640. The presentation may take the form of viewing a video and/or listening to audio on the agent workstation 120. The lesson presentation function 640 maintains a lesson log 650, which tracks the presentation of lessons 610 to agents. In one implementation the lesson log 650 includes an agent identifier, a lesson identifier, the time when the lesson presentation began, and an indication of whether the lesson has been completed.

In yet another point of integration between WFM 310 and the learning component 330, the lesson log 650 is provided to the adherence component 360 in the WFM 310. The adherence component 360 uses information in the lesson log 650 to determine whether an agent has met the lesion completion date. If not, the adherence component 360 notes the incomplete lesson as an exception to adherence.

Scheduling assigned lessons and tracking adherence to these assignments is further described in the U.S. patent application “Tracking of Lesson Adherence in a Call Center Environment,” Attorney Docket Number 762301-1150, filed the same day and by the same assignee as the instant application.

FIG. 7 shows several points of integration between the performance manager 340 and the learning component 330. The performance manager 340 maintains more key performance (KPIs) 710 that measure how well an agent or group of agents is performing. The KPIs 710 may be based one or more source measurements 720, such as evaluations from the quality monitor 320 and call statistics from call router 140 (e.g., call duration, hold time during call, etc.)

The performance manager 340 does analysis on the KPIs 710 and/or the source measurements 720 to produce scorecards 730. The analysis may include calculating statistics such as average, variation, etc., aggregating by time period or groups of agents, and determining trends. The scorecards 730 are then presented in visual form to a user. Examples of scorecards are a daily scorecard for an agent or a team, and a scorecard of all agents for the past month.

In the integrated system 700, the KPIs 710 are also provided (740) to the learning component 330. As described earlier, the learning component 330 maintains lessons 610, which can be assigned to an agent for review. In the integrated system 700, each lesson 610 is associated with one or more KPIs 710. The lesson assignment component 620 examines one or more of the KPIs 710 for a particular agent, and makes an assignment (750) for a lesson 610 associated with that KPI 710, based on criteria associated with a KPI or a competency. In one implementation, the criteria is a comparison of one or more KPIs 710 for an agent to threshold values, and the lesson assignment component 620 assigns a lesson 610 if the KPI 710 is lower than the threshold. This point of integration therefore allows automatic lesson assignment based on KPI 710.

Automatic lesson assignment is further described in the U.S. patent application “Integration of E-Learning and Scorecards in Call Center Operation,” Attorney Docket Number 762301-1090, filed the same day and by the same assignee as the instant application.

The presentation may also include a test that is given to the agent to determine competency the area associated with the lesson 610. In yet another point of integration between WFM 310 and the learning component 330, the agent test score 760 for an agent is provided to the performance manager 340. The performance manager 340 updates the KPIs 710 to reflect the agent competency described by the test score 760.

FIG. 8 shows a point of integration between the WFM 310 and the performance manager 340. Conventional schedulers allow agents to set preferences for shift assignments (e.g., one agent prefers to work weekends and another prefers to work nights). Since most agents are expected to prefer a day shift rather than a midnight shift, shift preferences are typically combined with agent ranking or seniority, so that someone works the midnight shift. This leads to a situation where the midnight shift is staffed with all of the “worst” agents.

As described earlier, the performance manager 340 maintains KPIs 710 that measure agent and/or group performance. In the integrated system 800 shown in FIG. 8, the scheduler 350 considers agent KPIs 710 when scheduling, so that some “good” agents are also added to the shift. The KPI 710 may reflect, for example, an evaluation of the agent's performance on a set of customer interactions. In one embodiment, the scoring is done by a human while playing back the recorded interaction. In another embodiment, the scoring is at least partly automated through the use of speech analytics.

The agent KPIs 710 are provided to the scheduler 350 in the WFM 310. Also provided to the scheduler 350 are quality goals 810 for a particular schedule interval. Examples of quality goals are “50% of agents have a score at of least 80” and “average score is at least 80.”

The scheduler 350 considers the quality goals 810 and the KPIs 710, along with other inputs, to determine a schedule 530, which includes agent assignments to work activities at specific times. The scheduler 350 also considers other inputs, such as a workload forecast 820, agent skill sets 830 and agent shift preferences 840. The scheduler 350 then chooses a mix of agents to work a shift, so that the agent scores combine to meet the quality goals 810. Integration of KPIs and the scheduler is further described in the U.S. patent application “Systems and Methods for Scheduling Call Center Agents Using Quality Data,” Attorney Docket Number 762301-1010, filed the same day and by the same assignee as the instant application.

FIG. 9 shows another point of integration between the WFM 310 and the performance manager 340. As described earlier, the performance manager 340 maintains KPIs 710 that measure agent and/or group performance, and produces scorecards 730 from the KPIs 710. The scorecards 730 provide a quick way for a manager to determine areas that require attention. For example, if a particular agent is out of adherence or has a low competency score, then the adherence or competency KPI can be flagged with a warning icon. Typically, the manager wants more detailed information about the flagged problem area. A conventional contact center solution requires the manager to open up a particular application, such as Adherence or Quality Monitoring, to obtain detailed information about the problem area. Once in the application, the manager must then navigate to the root cause of the problem (e.g., the activity that was out of adherence).

In contrast, the integrated system 900 allows a user to quickly view details associated with the flagged problem area, in the appropriate application context. Several examples of this use of application context are shown in FIG. 9. When interacting with the performance manager 340, selecting an adherence-related KPI (910) in a scorecard 730 brings the user to a view (920) of adherence information. Furthermore, the particular agent activities that resulted in the out-of-adherence flag 910 are highlighted or otherwise brought to the user's attention in the view 920. As another example, selecting a quality score-related KPI (930) brings the user to the quality monitor 320, and more specifically to the particular evaluation form 940 which contains the flagged quality score 930.

As yet another example, selecting a call statistic-related KPI (950), such as call duration or hold time, brings the user to the quality monitor 320. The quality monitor 320 presents a list of recorded interactions (from the interactions database 375) which contributed to, or are in someway related to, the flagged call-statistic score 950. The user can then play back (960) one of the recorded interactions. The integrated system 900 thus greatly simplifies root cause analysis for contact center personnel.

Call recording and monitoring are vital to contact center operations and the business. Every day, insight and feedback on the organization are gained from customer interactions. Valuable business intelligence can be extracted from these calls to help call center executives improve operational efficiency, customer satisfaction, and profitability. Yet management can only listen to a small segment of recorded calls. Managers must search manually through an enormous number of calls just to find the calls they need to analyze. The process is not only inefficient and expensive, but valuable information is continually ignored, leaving only a small sample of data needed to make informed business decisions.

Referring now to FIG. 10, with the analytics function 385 of the present invention (first introduced in FIG. 3), contact centers can now convert all call recordings into actionable business intelligence. Management can discern important competitive and business insight and easily identify trends from customer interactions, by analyzing speech, telephony, agent, and recording data together. In an enhancement, the analytics function 385 also streamlines the quality monitoring process by automatically classifying and scoring calls, based on selection criteria that may include any or part of the data captured by the integrated systems disclosed herein, including speech analytics.

The analytics function 385 of the present invention enables businesses to: (1) have a more accurate view of the customer experience, which allows executives across the organization uncover critical customer attitudes, needs, and requirements; (2) automatically score and classify calls for easy retrieval and examination, which enables call centers to digitally score calls to conduct automated quality and customer satisfaction surveys; and (3) discover trends related to customer behavior (e.g. churn, product adoption)that impact the business.

The analytics function 385 preferably uses speech recognition 1000 to convert the recorded calls into a searchable repository that allows for the query of words and/or phrases contained within the recorded calls. This repository may manifest itself as a text transcript or searchable phonetic model of the recorded calls. The analytics function 385 may apply additional unstructured data analysis techniques to refine and extract the context and further meaning from the conversations. Examples of various techniques that may be applied to refine the context of the mined speech, or the speech-to-text conversion, include: statistical modeling of grammar using a statistical model of grammar 1010 module; and natural language processing using a natural speech patterns 1020 module. Further, the analytics function 385 identifies the critical words and phrases within the context of the conversation. All this enables the embodiments disclosed herein to capture the intent of the call, rather than merely the words of the call.

In an alternative embodiment, the analytics function 385 converts the audio of the conversation into a phonetic representation of the call and uses a word-spotting method 1030 (or a query analysis), which flags or tags calls by a specific word, phrase, proximity, inflection, tempo, or emotion. Queries may be performed on an ad-hoc basis or stored for pattern analysis.

With the recorded calls converted to searchable content (via a transcribed call 1040 represented in FIG. 10), the analytics function 385 allows users to look back in time to discover what customers have said. In the preferred embodiment, users do not need to know in advance what they are looking for. For example, if there were a spike in call volume last week, the analytics function 385 can enable the contact center to understand the reason for the increased calls. Also, the user can incorporate metadata obtained from telephony or CRM systems to gain further insight into the reasons for the call spike.

In an enhancement, the analytics function 385 also uses a pattern recognition module 1050 to pull meaning out of the results generated by speech recognition. The pattern recognition module 1050 discerns the call's pattern and automatically places the call into one or several categories once the call is ingested into the speech engine, based on context the pattern recognition module 1050 is able to extract from the speech mining function. The patterns are used not only to classify calls but also to determine if a particular activity has occurred during the call, or to automatically score individual evaluation or survey questions based on this data. For instance a call score might be correlated to an existing evaluation or customer survey question during the call (e.g., “did the agent offer a cross sell”, “did the agent remember to read the corporate policy”). By automating the labor-intensive quality monitoring processes, contact centers can realize not just a fast return on investment, but also deploy resources where they are strategic to the call center.

The analytics function 385 can link the call content to the metadata from, for example, a quality monitoring component (see FIG. 3), to relate characteristics such as agent ID, time/date, speaker's name, workgroup ID, and call routing. The analytics function 385 can link to custom data sources that may contain other information related to the agent/customer interaction, for example, a CRM system.

The analytics function 385 also includes a search function 1060. An append feature in the search function allows the user to modify the initial search by tacking on additional criteria and logic. A refine feature function allows the user to add to the search criteria, which are then used on the results of the last search. A remove feature allows the user to modify the initial search by tacking on additional criteria and logic. An undo allows any of the modifications just described to be reversed. In one enhancement, results from the initial search string using the search function 1060 can be refined to help focus on particular calls of interest. In another enhancement, users can combine the search functionality described above with data from the CTI, ACD and other sources via a CTI ACD integration 1070 module.

Different individuals use different words or phrases to depict a similar meaning. Recognizing this fact, the analytics function 385 enables users to expand single words into complete concepts, which convey intent and meaning, rather than being tied to one narrow possibility. An expansion option 1080 allows users to include plural, synonym, homonym, and containing words, in a single clean screen. For example, instead of searching for the single word “bill”, the user can select to search for “bill, bills, account, charges, invoice, statement, billing, billed, bell”, which will most likely return better results because it takes into account the differences of expression.

In one enhancement, the expansion option 1080 allows for the identification of temporal relationships between words, phrases and other collected events in order to better identify the context of the conversation. For example, a search that includes the word “supervisor” in a temporal relationship with words like “transfer me to”, or in relationship to a call transfer event, can provide much more context than a search for “supervisor”. The expansion option allows users to capture more instances of the concept that they are exploring and furthermore establish the intent of the calls. This improves upon keyword-spotting technologies, which are not good enough to perform ad-hoc searching for concepts, which is the ultimate goal in content discovery.

The analytics function 385 further enables the user a variety of ways to derive insight from the search results. The Call Replay 1090 component allows the user to listen to an audio file from the search results, in part or its entirety. Playing a portion of the audio allows the user more efficiently go through the search results without having to waste time listening to the whole conversation. The Text Display 1092 component shows a continuous text for the entire recognized content when playing back part or all of a call. This allows users quickly capture terms and expressions exchanged in the call that might be of importance. The Save Searches 1094 component allows a user to save and easily retrieve searches for further refinement and analysis. The Export 1096 component allows search results to be exported to a wide variety of formats, such as Microsoft Excel or Adobe PDF format. The Search Statistics 1098 component displays information on the current search (e.g. calls counted, search time). In one enhancement, the analytics function 385 further includes call visualization component which includes an interface for displaying the text of a set of calls along with other data captured by the integrated system of the present invention along with integrated sources. A call visualization component is more fully described in the '705 application and incorporated by reference above).

Preferably, the analytics function 385 automatically classifies and scores calls via classify calls 1062 module and a score calls 1064 module. This feature can greatly reduce the time and effort that contact centers spend on the quality monitoring process by “structuring” unstructured voice recordings and categorizing them. The classify calls 1062 module preferably classifies calls based on the content. A call may be classified into one or more “buckets.” The analytics function 385 relies on the concept that all conversational threads have at their core one or more “patterns” of speech.

Patterns are complex descriptions of different ways that people communicate information, not just simple “words” for matching. These patterns of speech do not have to contain exact word matches for particular search terms, but they only “look” like a specific pattern. Each pattern is defined and assigned a weight by the pattern developer, and each area of intent is assigned a threshold. If a group of patterns match and their added weights exceed the threshold, then that conversation is said to “look” like and contain that intent.

The weights and threshold are user definable and therefore easily tweaked to produce better and more accurate results. A typical intent “bucket” will contain anywhere from five to 100 “patterns” to match. Patterns can be shared across domains and industries, and pattern bases can evolve forward to deliver ever more accurate and finely tuned pattern matching.

The analytics function 385 uses patterns not only to classify calls via the classify calls 1062 module, but also to evaluate if a particular activity occurred during a call via the score calls 1064 module. The user begins by designating the objective criteria on which the calls are to be scored into the application. A set of patterns is then described for the criteria. A call is then scored based on the extent to which the criteria patterns were fully met, partially met, or not met at all. Each weighted threshold for each score level can be customizable.

The analytics function 385 allows the user to create a graphical representation of trends found in the calls via a graphical representation 1066 module. This enables a user to view statistics about complex trends over a large time period.

The trend view displays a suite of ad-hoc reports that can be easily configured by the parameters in Table 1.

TABLE 1 Time/Day Interval Value to Calculate Segmentation Day of Week Avg # Words Per Call By Agent Month Avg Call Length In Seconds By Agent Group By Week Call Count By Content Group By Quarter Hit Total By Customer Account By Year Sum (WAVLength) By Department By Location

By visualizing the information by the parameters above, the user can gain a more detailed view on the particularities of the search phrases.

Another trending capability is the display of, for example, the top 200 words mentioned in the recorded calls (where the number of top words is customizable). The analytics function 385 proactively shows the words that are unusually more frequent than before or compared to the standard language. This acts as an “early warning system” to enable organizations to understand how the conversations have changed from one period to the next.

Preferably, the analytics function 385 organizes and delivers results customized to the end-users requirements via a reports 1068 module. In an enhancement, reports 1068 module allows for scheduling options that enable users the ability to vary frequency of report delivery so analysts can zoom in on critical data metrics hourly, daily, monthly, etc. Users can customize and automate reporting. Once a query is created, the user can save the query to run automatically. Users can create and view reports in different formats while using the web-based viewer. For example, reports can be output as Excel or PDF files, and then emailed. The reports are interactive, in that calls can be played back live from the results of the report. The reports 1068 module, which is preferably based on industry-standard databases such as SQL, can be used to customize reports, to extract, format and report from the underlying data. In another enhancement, the reports 1068 module is a dashboard reporting system which can, for example, link the actual calls detected for each event or report.

Coaching

As will be described in greater detail here, some embodiments of a work force optimization system can incorporate a coaching system. In this regard, such a coaching system can provide one or more of the following functions: providing supervisors, e.g., a manger, with an ability to open a coaching session; scheduling a coaching meeting within the agent's work schedule; enabling performance of the agent to be reviewed; allowing training to be scheduled; allowing performance goals for the agent to be set; producing a record the coaching session; reviewing contacts and interactions; and reviewing a later performance of the agent to track the results of the coaching session

As mentioned before, workforce optimization can include an analyze and investigate component 240 (FIG. 2), which can facilitate a determination regarding whether an agent is meeting pre-established criteria, e.g., whether the performance of an agent is out of adherence. In this regard, if it is determined that an agent is not meeting such pre-established criteria, embodiments of a coaching system can enable a supervisor, i.e., a person in a supervisory role with respect to the agent (e.g., the supervisor or a manager) to create a coaching session. Responsive to creation of such a coaching session, preparations for a coaching meeting can be undertaken, such as by aggregating various reports and corresponding data that can be used to educate an agent about the reason for the coaching session. A coaching meeting can then be scheduled.

During such a coaching meeting, potential deficiencies in an agent's skill set can be identified, thereby leading to the assignment of various learning. Goals also can be assigned during a coaching meeting with any assigned learning and goals being annotated on a coaching form. In some embodiments, an agent can be given an opportunity to provide feedback on the coaching session using the coaching form. In some embodiments, a coaching session is considered complete when the corresponding coaching form is annotated as such by the relevant supervisor. However, in some embodiments, the coaching session may not be deemed complete until further analysis is undertaken in order to determine whether the coaching session has resulted in improvements in agent performance.

In this regard, FIG. 11 is a flowchart depicting the functionality of a representative embodiment of a coaching system 1100. As shown in FIG. 11, the functionality (or method steps) may be construed as beginning at block 1102, in which a determination is made that an agent is not meeting one or more pre-established criteria. In block 1104, a coaching meeting is scheduled between a supervisor of the agent and the agent in order to this noted condition. In block 1106, training is scheduled for the agent based, at least in part, on information obtained during the coaching meeting such that information corresponding to the noted condition, coaching meeting and training is integrated into a coaching session form.

As should be noted, a host of integrations can be provided to facilitate coaching. By way of example, one such integration involves the manner in which a coaching session is initiated. In some embodiments, this can involve triggering events, the occurrence of which relates to data maintained by one or more other systems.

Another such integration involves associating data with a coaching session. In some embodiments, this can include attaching files and/or links to a coaching form that is used during the coaching session. These files and/or links can provide access to information obtained by other systems, such as recordings of relevant interactions involving the agent that is to be coached. In some embodiments, evaluations and/or interactions from a Quality Monitor, scorecards, lessons from eLearning and/or URLs can be attached.

The functionality of another embodiment of a coaching system 1200 is depicted in the flowchart of FIG. 12. As shown in FIG. 12, the functionality may be construed as beginning at block 1202, in which a coaching session with an agent is triggered. By way of example, the coaching session can be triggered by a quality monitoring application that has identified a bad evaluation corresponding to the agent. As another example, a coaching session can be triggered by a performance management application that identifies a key performance indicator (KPI) with an out-of-range condition. As such, the coaching session is triggered by an alert rule of the performance management application. As yet another example, triggering of a coaching session can be accomplished manually, such as by a user (e.g., a manager) actuating a graphical user interface associated with a coaching application. Additionally or alternatively, coaching sessions can be set periodically.

Regardless of the particular manner used to trigger a coaching session, the process proceeds to block 1204, in which a predefined coaching session form is provided. In some embodiments, the coaching form begins as a template with several fields. Notably, templates of various types could optionally be available for use in some embodiments.

In block 1206, at least a portion of the coaching session form is filled in with variable information. For instance, variable information corresponding to an agent can be used to fill in fields that relate to the agent. This information could be provided by one or more systems that maintain information corresponding to the agent. As another example, a manager can input textual notes relevant to the trigger that initiated coaching session. Additionally or alternatively, access to information associated with the coaching session can be appended to the form such as via links and/or attachments.

In block 1208, a coaching meeting is scheduled with the agent. Notably, in order to facilitate such scheduling, the coaching system accesses the agent's schedule and a determination is made as to when an appropriate coaching meeting can take place. Thus, in some embodiments, the meeting can be automatically scheduled, while in other embodiments information regarding the agent's existing schedule can be provided for review so that a suitable time for the coaching meeting can be selected.

In block 1210, various action items and/or information can be appended to the coaching session form responsive to conducting the coaching meeting between the supervisor and the agent. Such action items can include scheduling the agent for training, whereas the information can include meeting notes. It should be noted that when training is to occur, some embodiments are capable of automatically scheduling the training, such as by accessing a current schedule of the agent and correlating that schedule with the required training time.

Thereafter, such as depicted in block 1212, the agent is provided access to the coaching session form so that the agent can provide relevant comments. Thereafter, the supervisor conducting the coaching can complete the session after reviewing any agent comments. In some embodiments, one or more additional steps associated with analyzing whether the coaching session and/or corresponding follow-on training has improved agent performance can be conducted prior to completing the session.

Such a coaching system enables a supervisor to schedule, prepare and complete a coaching session and save information corresponding to the coaching session for future reference. As mentioned before, such a coaching session can incorporate one or more predefined coaching forms that are designed to aid a supervisor in providing coaching in a structured manner and to act as a record for the coaching session. Such a coaching form can be reviewed later to track progress against any actions or goals that were set during the coaching session.

As mentioned before, a supervisor can decide to create a coaching session from various points across several applications. By way of example, a manager can create a coaching session when viewing a scorecard, such as when the manager determines that the agent is below target. In some embodiments, opening of a coaching session selects a form that automatically populates the agent details and attaches a corresponding link, if appropriate. In this regard, if the coaching session was created during review of a particular scorecard, information corresponding to that scorecard can be automatically appended to the form, such as by providing a link on the coaching session form. Similarly, other types of information, e.g., recordings, lessons, reports, can be automatically appended to a coaching form when the coaching session is initiated during review of the corresponding information. For instance, when a manager is reviewing a recording for compliance and a discrepancy is noted that prompts the manager to initiate a coaching session, the recording can be automatically appended to the coaching session form.

Responsive to conducting a coaching meeting, various other information can be appended to a coaching form. For instance, a link associated with a company policy to which the agent did not adhere could be provided.

Another aspect of the coaching session form involves that of agent review, feedback and/or signature. In this regard, some embodiments can enable an agent to review the information contained in a coaching session form. Although this can be accomplished by merely providing the agent with full access to the coaching form, various levels of security can be provided. By way of example, each member of a company can be provided with a designated security level that provides a corresponding level of access to information associated with a coaching session form. For instance, in some embodiments, a manager can be granted full access, i.e., an ability to review and edit (e.g., during an established status period) all fields of the form, while an agent may only be granted limited access, e.g., an ability to review designated portions of the form and to provide feedback.

In this regard, coaching sessions typically exhibit different states as the sessions progress from creation to completion. The following exemplary states could allow for a coaching session to move through a pre-defined workflow including pending, published, coached, awaiting feedback, follow-up and complete.

With respect to the “pending” state, responsive to a coaching session being created, the state is set to pending. In the pending state, a coaching session form and any information, e.g. attachments, associated therewith may not be accessible to the relevant agent. That is, in the pending state, the manager that crated the coaching session and/or the manager to which coaching is assigned (and/or other personnel as defined by security level) may have access to the form. In the pending state, the manager creating the coaching session can have access to a form and is able to complete the form, such as by adding attachments as will be discussed later.

In the “published” state, access is provided to the agent that will be coached. However, the agent may not be provided with access that would be necessary for providing any comments. In this regard, the ability to provide comments may be limited, in some embodiments, until after a coaching meeting has occurred.

With respect to the “coached” state, this state can be set responsive to a coaching meeting taking place. Notably, during the aforementioned published state, which typically would be the state exhibited until the coaching meeting has occurred, the manager may be able to edit the coaching session form by providing notes and/or comments associated with the coaching meeting. Additional information, such as attachments and/or links associated with training that is to be completed by the agent also can be provided to the form during the published state.

After the coached state has been achieved, an optional “awaiting feedback” state can be provided during which an agent is afforded any opportunity to provide feedback regarding the coaching. This feedback can include adding comments to the coaching session form and/or a digital signature to the form that tends to authenticate that the coaching has occurred.

With respect to the “follow-up” state, this state also can be set responsive to a coaching meeting taking place. However, in contrast to the awaiting feedback state, the follow-up state does not end until verification can be made regarding the success of the coaching meeting and/or associated training. In this regard, the status may be set to follow-up until subsequent KPIs are reviewed.

Once the coaching session is completed, the “complete” state can be set. Notably, however, various criteria can be used for achieving the complete state. In some embodiments, the manager of the agent can set the state to complete after any feedback comments have been reviewed. In other embodiments, additional information regarding agent performance may be acquired and analyzed to determine the effectiveness of the coaching. Thus, in these embodiments, the complete state is set after the follow-up status has terminated.

Regardless of the particular criteria used for determining whether the session is complete, once the complete state has been achieved, edits to the coaching session form can be prevented thereby preserving a record of the coaching session. In some embodiments, if it is determined that a coaching session form needs to be changed after achieving the complete state, a higher access level than that of the manager facilitating the coaching may be required.

Exemplary graphical user interfaces associated with an embodiment of a coaching system will now be described with respect to FIGS. 13-23. In this regard, the graphical user interface (GUI) 1300 of FIG. 13 incorporates a Coaching icon 1302 that, when selected, presents a My Coaching tab 1304 and a Manage Coaching tab 1306. The Manage Coaching tab is depicted in FIG. 13.

The Manage Coaching tab depicted in FIG. 13 incorporates a Coaching Sessions pane 1310 that depicts all the coaching sessions in which the logged-in user, in this case a manager, is involved. In this regard, the Coaching Sessions pane can be populated with information that is selected based upon organization name, with a list of such organization names appearing in the Organization Name selector list 1312. Information in the Coaching Sessions pane also can be filtered by status (field 1314) and/or dates (field 1316). In some embodiments, selecting a particular agent can present coaching sessions associated with that agent in the Coaching Sessions pane. In FIG. 13, however, all agents associated with the organization “Hawaii” are displayed. Notably, a Create button 1320 is used for opening a new coaching session.

Responsive to actuating the Create button, the GUI displays information such as depicted in FIG. 14. As shown in FIG. 14, a coaching session has been created for an agent by the name of “Simple Pacific.” Some of the various fields associated with the coaching session include Agent Name field 1402, Job Title field 1404, Organization Name field 1406, Creation Date field 1408, Created by filed 1410, Due Date field 1412, Completion Date field 1414, Schedule Details field 1416, Coaching Session Type field 1418, Coach field 1420 and Status field 1422. In some embodiments, at least some of the aforementioned variable fields can be filled in automatically by the coaching system. By way of example, when an agent has been designated for a coaching session, the Job Title, Organization Name, Creation Date and Created by fields can be automatically filled in.

Various relationships between the various fields also can be present. By way of example, note that the Coaching Session Type is indicated as “Quarterly.” As a predefined rule, a quarterly coaching session can be established as a thirty-minute session, for example, thereby enabling the Schedule Details field to be filled in automatically with a thirty-minute time increment. Note also that integration with a scheduler can facilitate that such a thirty-minute coaching session is automatically placed in the agent's schedule.

FIG. 14 also depicts a Coaching Form section 1430 that includes an indication of evaluation form type (1432) and evaluation form name (1434). As mentioned before, in some embodiments, various form types of forms (or templates) can be established that are user selectable and/or automatically selected based on the type of coaching session.

An attachment section 1440 also is provided in the embodiment of FIG. 14, in which information corresponding to the coaching session can be provided. Note that web-based information can be provided in this section, e.g., note the URL designation. Additionally, note that an Edit Evaluation Form button 1450 is provided that can provide edit functionality based on access/security level of the user.

FIG. 15 depicts a window 1500 that can be displayed responsive to a user selecting to add an attachment of type “Evaluation.” On a per day basis established by a date field 1502, the user is enabled to view a list of evaluations of the agent that is being coached. This is accomplished by accessing a drop down menu 1504. Clicking on a View button provides additional details corresponding to the selected evaluation so that the user can ensure that the appropriate evaluation is being attached. Subsequent to selecting the appropriate evaluation, the user can click on the Attach button 1506, thereby attaching the selected evaluation to the coaching session.

In contrast, FIG. 16 depicts a window 1600 that can be displayed responsive to a user selecting to add an attachment of type “Scorecard.” In this regard, the user can select a scorecard of an agent that is to be coached based on one or more KPIs. By selecting the appropriate one or more KPIs from a drop down menu 1602 during a relevant date range (1604) and clicking the View button, a scorecard page can be displayed so that the user can ensure that the appropriate scorecard is being attached. Thereafter, the user can click on the Attach button 1606, thereby attaching the selected scorecard to the coaching session.

FIG. 17 depicts a window 1700 that can be displayed to a user responsive to selecting to add an attachment of type “URL.” In this regard, the user is provided with a field 1702, in which a URL can be entered. A View button 1704 can then be actuated in order to permit viewing of the entered URL. Thereafter, an Attach button 1706 can be actuated, thereby attaching a link to the URL to the coaching session.

FIG. 18 depicts a window 1800 that can be displayed to a user responsive to the user selecting to add an attachment of type “Interaction.” By way of example, such an interaction can include information such as screen capture data and/or recordings associated with an interaction with which the agent was involved. Typically, such an interaction relates to a communication session occurring between the agent and a customer. Selecting of an interaction for attachment to a coaching session is facilitated by selecting a date (1802), the selection of which presents all interactions with which the agent is associated on that date within an Interactions pane 1804. Selection of a particular interaction and subsequent actuating of a View button 1806 permits information associated with the selected interaction to be displayed to the user in order to determine whether that interaction should be appended to the coaching session. Once the appropriate one or more interactions have been designated, the interactions can be attached to the coaching session by actuating Attach button 1808.

FIG. 19 depicts a window 1900 that can be displayed to a user responsive to the user selecting to add an attachment of type “Lesson.” As shown in FIG. 19, the user is presented with the name of the employee (1902) and organization (1904), as well as a list of lessons (1906) that are available to the agent. By selecting the appropriate lesson and actuating Attach button 1908, the designated lesson will be attached to the coaching session. Notably, selecting a lesson via the coaching system in some embodiments can automatically assign that lesson to the agent. That is, the lesson will appear as a scheduled event on the agent's schedule. In some embodiments, a view lesson link can be provided that provides access corresponding to the current status of the lessons assigned to an agent.

In this regard, FIG. 20 depicts a window 2000 that can be displayed to a user when such a view lesson link has been actuated. Notably, the Lesson Status field 2002 indicates that the assigned lesson is “In Progress.” That is, the lesson has been assigned and has not yet been completed by the agent. Notably, this (as well as other GUI functionality described with respect to the depicted embodiments) can be accomplished in other manners. By way of example, in contrast to providing separate windows, drill down functionality can be employed.

FIG. 21 relates to the My Coaching tab 1304 (FIG. 13). In this regard, the My Coaching tab is able to display all coaching sessions associated with the logged-in user. For instance, in some embodiments, the My Coaching tab can display coaching sessions assigned to an agent when the agent is logged in, can display coaching sessions for which the employee is the coach when the employee is logged in, and/or can display coaching sessions that were scheduled by a manger when the manager is logged in. In the example GUI of FIG. 21, the My Coaching window provides information associated with the date assigned (2102), the due date (2104), the review type (2106), who is assigned to perform the coaching (2108) and who assigned the coaching (2110).

The My Coaching tab also can display information corresponding to a particular coaching session, provided that the logged-in user has access. As shown in FIG. 22, the information corresponds to that which was displayed in FIG. 13, i.e., the coaching session form is displayed.

FIG. 23 is a hardware block diagram of a general-purpose computer 2300 that can be used to implement one or more of the components of the integrated contact center system 300 disclosed herein, or the integrated contact center processes or methods disclosed herein. The computer 2300 contains a number of components that are well known in the art of call center software, including a processor 2310, a network interface 2320, memory 2330, and non-volatile storage 2340. Examples of non-volatile storage include, for example, a hard disk, flash RAM, flash ROM, EEPROM, etc. These components are coupled via a bus 2350. The memory 2330 contains instructions which, when executed by the processor 2310, implement the methods and systems disclosed herein. Omitted from FIG. 23 are a number of conventional components, known to those skilled in the art that are unnecessary to explain the operation of the system 2300.

The systems and methods disclosed herein can be implemented in software, hardware, or a combination thereof. In some embodiments, the system and/or method is implemented in software that is stored in a memory and that is executed by a suitable microprocessor (μP) situated in a computing device. However, the systems and methods can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device. Such instruction execution systems include any computer-based system, processor-containing system, or other system that can fetch and execute the instructions from the instruction execution system. In the context of this disclosure, a “computer-readable medium” can be any means that can contain, store, communicate, propagate, or transport the program for use by, or in connection with, the instruction execution system. The computer readable medium can be, for example but not limited to, a system or propagation medium that is based on electronic, magnetic, optical, electromagnetic, infrared, or semiconductor technology.

Specific examples of a computer-readable medium using electronic technology would include (but are not limited to) the following: an electrical connection (electronic) having one or more wires; a random access memory (RAM); a read-only memory (ROM); an erasable programmable read-only memory (EPROM or Flash memory). A specific example using magnetic technology includes (but is not limited to) a portable computer diskette. Specific examples using optical technology include (but are not limited to) optical fiber and compact disc read-only memory (CD-ROM).

It should be noted that any process descriptions or blocks in flowcharts should be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process. As would be understood by those of ordinary skill in the art of the software development, alternative embodiments are also included within the scope of the disclosure. In these alternative embodiments, functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved.

This description has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Obvious modifications or variations are possible in light of the above teachings. The embodiments discussed, however, were chosen to illustrate the principles of the disclosure, and its practical application. The disclosure is thus intended to enable one of ordinary skill in the art to use the disclosure, in various embodiments and with various modifications, as are suited to the particular use contemplated. All such modifications and variation are within the scope of this disclosure, as determined by the appended claims when interpreted in accordance with the breadth to which they are fairly and legally entitled. 

1. A method for coaching an agent of a contact center comprising: receiving information corresponding to a condition indicating that the agent is failing to meet pre-established criteria; scheduling a coaching meeting with the agent in order to address the condition; and scheduling training for the agent based, at least in part, on information obtained during the coaching meeting, the training being selected to address the condition; wherein the information corresponding to the condition, the information corresponding to the coaching meeting, and information corresponding to the training is integrated into a coaching session form.
 2. The method of claim 1, wherein receiving the information comprises automatically receiving the information from a workforce optimization application.
 3. The method of claim 1, wherein the information corresponding to the condition is integrated into the coaching session form as a link, the link providing access via the form to the information corresponding to the condition.
 4. The method of claim 1, wherein scheduling a coaching meeting comprises: accessing information corresponding to a current schedule of the agent; and selecting an available time within the current schedule for conducting the coaching meeting.
 5. The method of claim 1, wherein the scheduling of a coaching meeting is performed automatically based, at least in part, upon a current schedule of the agent and an anticipated time required for performing the coaching meeting.
 6. The method of claim 1, wherein scheduling training comprises: accessing information corresponding to a current schedule of the agent; and selecting an available time within the current schedule for conducting the training.
 7. The method of claim 1, wherein the scheduling of training is performed automatically based, at least in part, upon a current schedule of the agent and an anticipated time required for performing the training.
 8. The method of claim 1, further comprising providing access to the coaching session form to the agent.
 9. The method of claim 8, wherein providing access is limited to review access and an ability to provide feedback.
 10. The method of claim 1, further comprising completing the coaching session responsive to a manager reviewing any feedback provided by the agent to the coaching meeting.
 11. The method of claim 1, further comprising completing the coaching session responsive to analyzing agent performance subsequent to the coaching meeting and any scheduled training.
 12. A method for coaching an agent of a contact center comprising: receiving information corresponding to a condition indicating that the agent is failing to meet pre-established criteria; responsive to receiving the information, accessing a coaching session template; automatically filling in at least a portion of the coaching session template with information corresponding to the agent such that a communication session form associated with the agent is generated; scheduling a coaching meeting with the agent in order to address the condition; selecting a training session based, at least in part, on information obtained during the coaching meeting; and automatically scheduling the agent for the training session based, at least in part, on information corresponding to a current schedule of the agent and an estimated time for conducting the training session such that the information corresponding to the condition, the information corresponding to the coaching meeting, and information corresponding to the training session is integrated into the coaching session form.
 13. The method of claim 12, wherein: the method further comprises displaying the coaching session form via a display device; and the information corresponding to the condition is integrated into the coaching session form as a link, the link providing access via the form to the information corresponding to the condition.
 14. The method of claim 13, wherein the condition relates to a bad score on a scorecard of the agent, and the link provides access to the scorecard.
 15. A system for coaching an agent of a contact center comprising: a coaching system configured to communicate with at least one application of a workforce optimization platform via a communication network, the coaching system being operative to: receive information corresponding to a condition indicating that the agent is failing to meet pre-established criteria; schedule a coaching meeting with the agent in order to address the condition; schedule training for the agent based, at least in part, on information obtained during the coaching meeting, the training being selected to address the condition; and integrate the information corresponding to the condition, the information corresponding to the coaching meeting, and information corresponding to the training into a coaching session form.
 16. The system of claim 15, wherein: the system further comprises a database of coaching form templates; and the coaching system is operative to access the database responsive to receiving the information corresponding to the condition, to generate a coaching session form corresponding to one of the templates and to fill in at least a portion of the coaching session form with information corresponding to the agent.
 17. The system of claim 15, wherein the coaching system is operative to schedule the coaching meeting automatically based, at least in part, upon a current schedule of the agent and an anticipated time required for performing the coaching meeting.
 18. The system of claim 15, wherein the coaching system is operative to enable a user to: access information corresponding to a current schedule of the agent; and select an available time within the current schedule for conducting the training.
 19. The system of claim 15, wherein the coaching system is operative to schedule the training automatically based, at least in part, upon a current schedule of the agent and an anticipated time required for performing the training responsive to receiving information indicating that the agent is to be scheduled for the training. 